Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Graciela Boente"'
Publikováno v:
Revstat Statistical Journal, Vol 12, Iss 2 (2014)
There is a vast literature on robust estimators, but in some situations it is still not easy to make inferences, such as confidence regions and hypothesis testing. This is mainly due to the following facts. On one hand, in most situations, it is diff
Externí odkaz:
https://doaj.org/article/ab8cbdf9c87a43cf8c906ce494559ac4
Autor:
Ana M. Bianco, Graciela Boente
Publikováno v:
Econometrics and Statistics.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::77c80e03a965b24e9ff879492e2bc4fe
https://doi.org/10.1016/j.ecosta.2023.04.001
https://doi.org/10.1016/j.ecosta.2023.04.001
Publikováno v:
Statistical Methods & Applications.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f0cba866c4998e1e73a5e5fdc7984f55
https://doi.org/10.1007/s10260-022-00680-z
https://doi.org/10.1007/s10260-022-00680-z
Publikováno v:
TEST. 31:563-594
Sparse covariates are frequent in classification and regression problems where the task of variable selection is usually of interest. As it is well known, sparse statistical models correspond to situations where there are only a small number of nonze
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::374c03caa69afbfe1b4340c45667b889
https://doi.org/10.1007/s11749-021-00792-w
https://doi.org/10.1007/s11749-021-00792-w
Publikováno v:
Robust and Multivariate Statistical Methods ISBN: 9783031226861
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a99e4f2f69741e54e27f59274cd3d403
https://doi.org/10.1007/978-3-031-22687-8_15
https://doi.org/10.1007/978-3-031-22687-8_15
Autor:
Graciela Boente, Daniela Laura Parada
Publikováno v:
Daniela Laura Parada
Functional quadratic regression models postulate a polynomial relationship between a scalar response rather than a linear one. As in functional linear regression, vertical and specially high-leverage outliers may affect the classical estimators. For
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::019b092ffd9d443708b8e20698415429
http://arxiv.org/abs/2209.02742
http://arxiv.org/abs/2209.02742
Publikováno v:
Journal of Nonparametric Statistics. 32:915-939
This paper deals with robust marginal estimation under a general regression model when missing data occur in the response and also in some covariates. The target is a marginal location parameter given through an M-functional. To obtain robust Fisher-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e10b198fbdcef82d37ca329b3e661908
https://doi.org/10.1080/10485252.2020.1834108
https://doi.org/10.1080/10485252.2020.1834108
Publikováno v:
Electronic Journal of Statistics. 16
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6c08e2d3faa358c02f5e90caf7481f2
https://doi.org/10.1214/22-ejs2042
https://doi.org/10.1214/22-ejs2042
Publikováno v:
Annals of the Institute of Statistical Mathematics. 72:855-893
This paper develops a robust profile estimation method for the parametric and nonparametric components of a single-index model when the errors have a strongly unimodal density with unknown nuisance parameter. We derive consistency results for the lin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::51a3d9f0ba64eb736cfdff06856af0e3
https://doi.org/10.1007/s10463-019-00712-8
https://doi.org/10.1007/s10463-019-00712-8
Publikováno v:
Journal of Multivariate Analysis. 170:46-62
In this paper, we propose robust estimators for the first canonical correlation and directions of random elements on Hilbert separable spaces by combining sieves and robust association measures, leading to Fisher-consistent estimators for appropriate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85695d4670c2a67c1a0273a755c1bc10
https://doi.org/10.1016/j.jmva.2018.03.003
https://doi.org/10.1016/j.jmva.2018.03.003